Accuracy and Precision of Wearable-Derived Gait Parameters: How these Affect the Performance of Models for Fall Prediction in the Elderly.

Abstract

Wearable sensors are widely used to assess spatiotemporal gait parameters and their variability, which are critical for fall risk prediction. However, the impact of gait analysis accuracy and precision on fall risk prediction remains unexplored. This study collected gait data from 95 older adults using instrumented footwear on an instrumented walkway which is recognized as a system with gold standards during the 6-minute walking test. Participants were classified into fallers and non-fallers based on retrospective fall history (falls in the 6 months prior to completing the experiment), prospective fall occurrence (falls in the subsequent 6 months after completing the experiment), and a combination of both. Gait parameters and their variability were estimated using three algorithms: the conventional foot displacement method and two support vector regression (SVR) techniques. These features were used to develop fall risk prediction models with four machine learning classifiers: logistic regression, decision tree, support vector machine, and artificial neural network. Our findings demonstrate that the accuracy and precision of gait analysis algorithms significantly influences the estimation of gait parameters and their variability, directly impacting fall risk prediction performance. Using a support vector classifier, the area under the receiver operating characteristic curve (AUC) values for predicting retrospective falls, prospective falls, and either fall type increased from 0.79, 0.84, and 0.77 (conventional method) to 0.85, 0.89, and 0.83 (SVR). These findings show the importance of refining gait analysis accuracy and precision in future studies that aim to use wearable sensors for fall risk assessment in older adults.